Spatial Regression Models: A Systematic Comparison of Different Model Specifications Using Monte Carlo Experiments

Autor/in	Rüttenauer, Tobias
Titel	Spatial Regression Models: A Systematic Comparison of Different Model Specifications Using Monte Carlo Experiments
Quelle	In: Sociological Methods & Research, 51 (2022) 2, S.728-759 (32 Seiten) PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Rüttenauer, Tobias)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0049-1241
DOI	10.1177/0049124119882467
Schlagwörter	Models; Monte Carlo Methods; Social Science Research; Data Analysis; Comparative Analysis; Accuracy; Error of Measurement; Regression (Statistics) + Suchen Sie Ihr Suchwort? Analogiemodell; Monte-Carlo-Methode; Social scientific research; Sozialwissenschaftliche Forschung; Auswertung; Messfehler; Regression; Regressionsanalyse
Abstract	Spatial regression models provide the opportunity to analyze spatial data and spatial processes. Yet, several model specifications can be used, all assuming different types of spatial dependence. This study summarizes the most commonly used spatial regression models and offers a comparison of their performance by using Monte Carlo experiments. In contrast to previous simulations, this study evaluates the bias of the impacts rather than the regression coefficients and additionally provides results for situations with a nonspatial omitted variable bias. Results reveal that the most commonly used spatial autoregressive and spatial error specifications yield severe drawbacks. In contrast, spatial Durbin specifications (SDM and SDEM) and the simple spatial lag of X (SLX) provide accurate estimates of direct impacts even in the case of misspecification. Regarding the indirect "spillover" effects, several--quite realistic--situations exist in which the SLX outperforms the more complex SDM and SDEM specifications. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01